Prompt Engineering
Tutorial
Prompt Engineering
The art of communicative design for Large Language Models.
Speaking to Shifting Landscapes
Prompt engineering is the strategic construction of input to guide model behavior without weight updates.
Level 1 — The 'CO-STAR' Framework
Effective prompts usually include these elements:
- C (Context): Background info.
- O (Objective): The specific task.
- S (Style): Writing tone (e.g., academic, funny).
- T (Tone): Emotional quality.
- A (Audience): Who is this for?
- R (Response): Format (JSON, Table).
Level 2 — Reasoning Chains
Chain-of-Thought (CoT) prompting involves asking the model to "Think Step-by-Step." This forces it to use its internal reasoning buffer before committing to a final answer.
Level 3 — Programmatic Prompting (DSPy)
In advanced NLP engineering, we stop writing manual prompts. We use frameworks like DSPy to automatically "compile" the best prompts based on evaluation metrics, treating prompts like code instead of "vibe-based" text.
Chain-of-Thought Example
PROMPT:
"Roger has 5 tennis balls. He buys 2 more cans of tennis balls.
Each can has 3 tennis balls. How many tennis balls does he have now?
Think through this step-by-step before giving the final answer."
EXPECTED OUTPUT:
"1. Roger starts with 5 balls.
2. He buys 2 cans, each with 3 balls, so 2 * 3 = 6 new balls.
3. 5 + 6 = 11.
Final Answer: 11"